longhorned beetle
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Author(s):  
Jean J. Turgeon ◽  
Michael T Smith ◽  
John Pedlar ◽  
Ronald Edward Fournier ◽  
Mary Orr ◽  
...  

Two breeding populations of the non-native Asian longhorned beetle (Anoplophora glabripennis Motschulsky), a pest of broadleaf trees in its native China, were discovered in Ontario in 2003 and 2013, respectively. Both populations were eradicated by removing all trees injured by the beetle and all uninjured trees deemed at high risk of injury. We used data collected during this removal to study host selection. Signs of A. glabripennis injury were observed on 732 stems from seven (i.e., Acer, Salix, Populus, Betula, Ulmus, Fraxinus and Tilia) of the 45 tree genera available. Complete beetle development was confirmed on only the first four of these seven genera. Most signs of injury were on the genus Acer and on trees with a diameter at 130 cm above ground ranging between 15 cm and 40 cm. On most trees, the lowest sign of injury was within three meters of the ground or within 40% of tree height. Tree height explained 63% of the variance in the location of the lowest sign of injury. Initial attacks were typically near the middle of the tree and expanded both upward and downward with successive attacks over time. We discuss how these findings could improve survey efforts for A. glabripennis.


Insects ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1139
Author(s):  
Xingeng Wang ◽  
Melody A. Keena

The Asian longhorned beetle (ALB), Anoplophora glabripennis (Motschulsky) and citrus longhorned beetle (CLB), Anoplophora chinensis (Förster) (both Coleoptera: Cerambycidae: Lamiinae), are high-risk invasive pests that attack various healthy hardwood trees. These two species share some similar host plants and overlapping distributions in large parts of their native ranges in China and the Korean peninsula as well as similar reproductive behaviors. The original Anoplophora malasiaca (Thomson) occurs in Japan and has been synonymized as CLB (hereafter referred to JCLB). In this study, a 30-min behavioral observation of paired adults, followed by a four-week exposure to host bolts, showed that ALB could not successfully cross with CLB. Mating was observed between female CLB and male ALB but not between female ALB and male CLB, no laid eggs hatched. JCLB males successfully crossed with ALB females to produce viable eggs although the overall percentage of hatched eggs was lower than those from conspecific mating pairs. However, ALB males could not successfully cross with JCLB females. CLB and JCLB mated and produced viable hybrid offspring and the hybrid F1 offspring eggs were fertile. These results suggest an asymmetrical hybridization between ALB and JCLB, and that both CLB and JCLB might be considered as two subspecies with different hybridization potential with congeneric ALB. Given their potential impacts on ecosystems and many economically important tree hosts, invasion of these geographically isolated species (ALB and JCLB) or distant subspecies (CLB and JCLB) into the same region may facilitate potential hybridization, which could be a potential concern for the management of these two globally important invasive forest pests. Further studies are needed to determine if fertile hybrid offspring are capable of breeding continually or backcrossing with parental offspring successfully.


2021 ◽  
Vol 764 ◽  
Author(s):  
Antonio Santos-Silva ◽  
Martijn Van Roie ◽  
Merlijn Jocqué

An ongoing study of the longhorned beetle fauna in the cloud forests of Cusuco National Park revealed multiple additions to the Honduran fauna. Four new species are described: Heterachthes caceresae sp. nov. (Cerambycinae, Neoibidionini), Oreodera kawasae sp. nov. (Lamiinae, Acrocinini), Phrynidius guifarroi sp. nov. (Lamiinae, Apomecynini), and Strangalia lunai sp. nov. (Lepturinae, Lepturini). Additionally, Lagocheirus parvulus Casey, 1913 (Lamiinae, Acanthocinini) is revalidated as Lagocheirus araneiformis parvulus Casey, 1913 (Lagochirus [sic]). We recorded Arixiuna varians (Bates, 1881) (Lamiinae, Hemilophini) for the first time for Honduras. These findings confirm how poorly the invertebrate biodiversity of cloud forests is documented and hints at the large number of species we are losing with the ongoing deforestation.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Quan Zhou ◽  
Xudong Zhang ◽  
Linfeng Yu ◽  
Lili Ren ◽  
Youqing Luo

Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.


2021 ◽  
Vol 79 ◽  
pp. 99-114
Author(s):  
Stephanie Haddad ◽  
Nayeli Gutiérrez ◽  
Felipe A. Noguera ◽  
Seunggwan Shin ◽  
Petr Svacha ◽  
...  

Taxonomic placement of the enigmatic monotypic Mexican longhorned beetle genus Vesperoctenus Bates is examined through inclusion in and reanalysis of the dataset of Haddad et al. (2018, Systematic Entomology 43: 68–89). We describe and discuss the phylogenetic significance of the internal structures of a recently collected V. flohri female from the Sierra de la Laguna mountain range in Mexico, the same specimen from which phylogenomic data was generated. Our phylogenomic analyses (469 genes) recovered Vesperoctenus with maximal statistical support within the cerambyciform family Vesperidae, sister to Vesperus Dejean (Vesperinae). Vesperus + Vesperoctenus were recovered sister to Philinae, and collectively form a clade sister to Anoplodermatinae. Thus, we place V. flohri within Vesperidae: Vesperinae: Vesperoctenini based on analyses of large-scale phylogenomic data. Finally, we propose that the conservation status of V. flohri merits assessment.


2021 ◽  
Author(s):  
quan zhou ◽  
Xudong Zhang ◽  
Linfeng Yu ◽  
Lili Ren ◽  
Youqing Luo

Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms—Random Forest(RF), Support Vector Machine༈SVM༉, Classification And Regression Tree༈CART༉—were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.


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